Crypto_predictor.ipynb

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      "name": "Crypto predictor",
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        "<a href=\"https://colab.research.google.com/github/Michaelzats/Crypto-Predictor/blob/main/Crypto_predictor.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "#Stocks/Crypto price predictor "
      ],
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        "id": "7Z31u3Kktw6I"
      }
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      "cell_type": "markdown",
      "source": [
        "The following tools enables to quickly predict the price of the particular asset via Machine Learning Techniques implementation"
      ],
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        "id": "7Ec_nWOmt7cY"
      }
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        "# First we will import the necessary Library \n",
        "\n",
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import math\n",
        "import datetime as dt\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# For Evalution we will use these library\n",
        "\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, explained_variance_score, r2_score \n",
        "from sklearn.metrics import mean_poisson_deviance, mean_gamma_deviance, accuracy_score\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# For model building we will use these library\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from tensorflow.keras.layers import LSTM\n",
        "\n",
        "\n",
        "# For PLotting we will use these library\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from itertools import cycle\n",
        "import plotly.graph_objects as go\n",
        "import plotly.express as px\n",
        "from plotly.subplots import make_subplots"
      ]
    },
    {
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      "source": [
        "# Load our dataset \n",
        "# Note it should be in same dir\n",
        "\n",
        "\n",
        "maindf=pd.read_csv('BTC-USD.csv')"
      ],
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        "id": "IZCJL7qi_824"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "print('Total number of days present in the dataset: ',maindf.shape[0])\n",
        "print('Total number of fields present in the dataset: ',maindf.shape[1])"
      ],
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of days present in the dataset:  2813\n",
            "Total number of fields present in the dataset:  7\n"
          ]
        }
      ]
    },
    {
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      "source": [
        "maindf.shape"
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            "text/plain": [
              "(2813, 7)"
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          "execution_count": 56
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        "maindf.head()"
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              "         Date        Open        High         Low       Close   Adj Close  \\\n",
              "0  2014-09-17  465.864014  468.174011  452.421997  457.334015  457.334015   \n",
              "1  2014-09-18  456.859985  456.859985  413.104004  424.440002  424.440002   \n",
              "2  2014-09-19  424.102997  427.834991  384.532013  394.795990  394.795990   \n",
              "3  2014-09-20  394.673004  423.295990  389.882996  408.903992  408.903992   \n",
              "4  2014-09-21  408.084991  412.425995  393.181000  398.821014  398.821014   \n",
              "\n",
              "       Volume  \n",
              "0  21056800.0  \n",
              "1  34483200.0  \n",
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              "            Date          Open          High           Low         Close  \\\n",
              "2808  2022-05-26  29564.777344  29834.160156  28261.906250  29267.224609   \n",
              "2809  2022-05-27  29251.140625  29346.943359  28326.613281  28627.574219   \n",
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              "         Adj Close        Volume  \n",
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              "2809  28627.574219  3.658201e+10  \n",
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              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "maindf.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fd8SM7c9JUis",
        "outputId": "20321fb6-1068-428b-fa7c-8209db888ded"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2813 entries, 0 to 2812\n",
            "Data columns (total 7 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Date       2813 non-null   object \n",
            " 1   Open       2812 non-null   float64\n",
            " 2   High       2812 non-null   float64\n",
            " 3   Low        2812 non-null   float64\n",
            " 4   Close      2812 non-null   float64\n",
            " 5   Adj Close  2812 non-null   float64\n",
            " 6   Volume     2812 non-null   float64\n",
            "dtypes: float64(6), object(1)\n",
            "memory usage: 154.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "maindf.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "xUXIAg6fJV9U",
        "outputId": "b76358e5-7d4c-4cb4-99fa-793b933c571d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Open          High           Low         Close     Adj Close  \\\n",
              "count   2812.000000   2812.000000   2812.000000   2812.000000   2812.000000   \n",
              "mean   12272.390742  12593.591198  11916.085923  12281.299128  12281.299128   \n",
              "std    16630.665424  17062.033163  16133.409345  16628.836068  16628.836068   \n",
              "min      176.897003    211.731003    171.509995    178.102997    178.102997   \n",
              "25%      619.221481    628.285019    613.341491    621.922760    621.922760   \n",
              "50%     6486.149902   6591.875000   6390.630127   6488.974853   6488.974853   \n",
              "75%    11298.761474  11532.533935  10966.187500  11302.831055  11302.831055   \n",
              "max    67549.734375  68789.625000  66382.062500  67566.828125  67566.828125   \n",
              "\n",
              "             Volume  \n",
              "count  2.812000e+03  \n",
              "mean   1.523247e+10  \n",
              "std    1.994205e+10  \n",
              "min    5.914570e+06  \n",
              "25%    8.466660e+07  \n",
              "50%    5.545056e+09  \n",
              "75%    2.594649e+10  \n",
              "max    3.509679e+11  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2812.000000</td>\n",
              "      <td>2812.000000</td>\n",
              "      <td>2812.000000</td>\n",
              "      <td>2812.000000</td>\n",
              "      <td>2812.000000</td>\n",
              "      <td>2.812000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>12272.390742</td>\n",
              "      <td>12593.591198</td>\n",
              "      <td>11916.085923</td>\n",
              "      <td>12281.299128</td>\n",
              "      <td>12281.299128</td>\n",
              "      <td>1.523247e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>16630.665424</td>\n",
              "      <td>17062.033163</td>\n",
              "      <td>16133.409345</td>\n",
              "      <td>16628.836068</td>\n",
              "      <td>16628.836068</td>\n",
              "      <td>1.994205e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>176.897003</td>\n",
              "      <td>211.731003</td>\n",
              "      <td>171.509995</td>\n",
              "      <td>178.102997</td>\n",
              "      <td>178.102997</td>\n",
              "      <td>5.914570e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>619.221481</td>\n",
              "      <td>628.285019</td>\n",
              "      <td>613.341491</td>\n",
              "      <td>621.922760</td>\n",
              "      <td>621.922760</td>\n",
              "      <td>8.466660e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6486.149902</td>\n",
              "      <td>6591.875000</td>\n",
              "      <td>6390.630127</td>\n",
              "      <td>6488.974853</td>\n",
              "      <td>6488.974853</td>\n",
              "      <td>5.545056e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>11298.761474</td>\n",
              "      <td>11532.533935</td>\n",
              "      <td>10966.187500</td>\n",
              "      <td>11302.831055</td>\n",
              "      <td>11302.831055</td>\n",
              "      <td>2.594649e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>67549.734375</td>\n",
              "      <td>68789.625000</td>\n",
              "      <td>66382.062500</td>\n",
              "      <td>67566.828125</td>\n",
              "      <td>67566.828125</td>\n",
              "      <td>3.509679e+11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "          const element = document.querySelector('#df-219a232b-0b85-4566-a5fe-8b8467226acd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for Null Values\n",
        "print('Null Values:',maindf.isnull().values.sum())\n",
        "print('NA values:',maindf.isnull().values.any())\n",
        "\n",
        "# If dataset had null values \n",
        "maindf=maindf.dropna()\n",
        "print('Null Values:',maindf.isnull().values.sum())\n",
        "print('NA values:',maindf.isnull().values.any())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r502hz73Jc0Y",
        "outputId": "c8b34c1a-0f51-4254-d79a-af5fd80603e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Null Values: 6\n",
            "NA values: True\n",
            "Null Values: 0\n",
            "NA values: False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Printing the start date and End date of the dataset\n",
        "\n",
        "sd=maindf.iloc[0][0]\n",
        "ed=maindf.iloc[-1][0]\n",
        "\n",
        "\n",
        "print('Starting Date',sd)\n",
        "print('Ending Date',ed)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G2Fr__gPJxOY",
        "outputId": "8a8fcded-7a34-4384-f59c-3addfc00febe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting Date 2014-09-30\n",
            "Ending Date 2022-05-30\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Analysis from 2014-2022\n",
        "maindf['Date'] = pd.to_datetime(maindf['Date'], format='%Y-%m-%d')\n",
        "\n",
        "y_overall = maindf.loc[(maindf['Date'] >= '2014-09-17')\n",
        "                     & (maindf['Date'] <= '2022-05-01')]\n",
        "\n",
        "y_overall.drop(y_overall[['Adj Close','Volume']],axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "9Y8oMaGsJyTo",
        "outputId": "68258dd1-94bf-4b5a-8a3d-0498c28f3dcb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date          Open          High           Low         Close\n",
              "0    2014-09-17    465.864014    468.174011    452.421997    457.334015\n",
              "1    2014-09-18    456.859985    456.859985    413.104004    424.440002\n",
              "2    2014-09-19    424.102997    427.834991    384.532013    394.795990\n",
              "3    2014-09-20    394.673004    423.295990    389.882996    408.903992\n",
              "4    2014-09-21    408.084991    412.425995    393.181000    398.821014\n",
              "...         ...           ...           ...           ...           ...\n",
              "2779 2022-04-27  38120.300781  39397.917969  37997.312500  39241.121094\n",
              "2780 2022-04-28  39241.429688  40269.464844  38941.421875  39773.828125\n",
              "2781 2022-04-29  39768.617188  39887.269531  38235.535156  38609.824219\n",
              "2782 2022-04-30  38605.859375  38771.210938  37697.941406  37714.875000\n",
              "2783 2022-05-01  37713.265625  38627.859375  37585.789063  38469.093750\n",
              "\n",
              "[2784 rows x 5 columns]"
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              "      <th>Date</th>\n",
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              "      <th>High</th>\n",
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              "      <th>Close</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>2014-09-17</td>\n",
              "      <td>465.864014</td>\n",
              "      <td>468.174011</td>\n",
              "      <td>452.421997</td>\n",
              "      <td>457.334015</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-09-18</td>\n",
              "      <td>456.859985</td>\n",
              "      <td>456.859985</td>\n",
              "      <td>413.104004</td>\n",
              "      <td>424.440002</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-09-19</td>\n",
              "      <td>424.102997</td>\n",
              "      <td>427.834991</td>\n",
              "      <td>384.532013</td>\n",
              "      <td>394.795990</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-09-20</td>\n",
              "      <td>394.673004</td>\n",
              "      <td>423.295990</td>\n",
              "      <td>389.882996</td>\n",
              "      <td>408.903992</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-09-21</td>\n",
              "      <td>408.084991</td>\n",
              "      <td>412.425995</td>\n",
              "      <td>393.181000</td>\n",
              "      <td>398.821014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2779</th>\n",
              "      <td>2022-04-27</td>\n",
              "      <td>38120.300781</td>\n",
              "      <td>39397.917969</td>\n",
              "      <td>37997.312500</td>\n",
              "      <td>39241.121094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2780</th>\n",
              "      <td>2022-04-28</td>\n",
              "      <td>39241.429688</td>\n",
              "      <td>40269.464844</td>\n",
              "      <td>38941.421875</td>\n",
              "      <td>39773.828125</td>\n",
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              "    <tr>\n",
              "      <th>2781</th>\n",
              "      <td>2022-04-29</td>\n",
              "      <td>39768.617188</td>\n",
              "      <td>39887.269531</td>\n",
              "      <td>38235.535156</td>\n",
              "      <td>38609.824219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2782</th>\n",
              "      <td>2022-04-30</td>\n",
              "      <td>38605.859375</td>\n",
              "      <td>38771.210938</td>\n",
              "      <td>37697.941406</td>\n",
              "      <td>37714.875000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2783</th>\n",
              "      <td>2022-05-01</td>\n",
              "      <td>37713.265625</td>\n",
              "      <td>38627.859375</td>\n",
              "      <td>37585.789063</td>\n",
              "      <td>38469.093750</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2784 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-40e5ffc3-fc17-4ff2-a70c-29cda40d8780')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "monthvise= y_overall.groupby(y_overall['Date'].dt.strftime('%B'))[['Open','Close']].mean()\n",
        "new_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', \n",
        "             'September', 'October', 'November', 'December']\n",
        "monthvise = monthvise.reindex(new_order, axis=0)\n",
        "monthvise"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "qUIvh-XQJ8vW",
        "outputId": "5cbbfb62-bed1-49fc-9ddb-fc5c38ab077c"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "                   Open         Close\n",
              "Date                                 \n",
              "January    12855.131425  12828.374881\n",
              "February   13793.306352  13869.217293\n",
              "March      14790.177346  14833.352724\n",
              "April      15132.974493  15119.381881\n",
              "May        10783.555304  10708.139980\n",
              "June        9299.305977   9294.420703\n",
              "July        9285.402500   9330.128271\n",
              "August     11312.971706  11345.157739\n",
              "September  10489.365578  10462.378150\n",
              "October    11321.578327  11416.077925\n",
              "November   12542.362183  12537.441752\n",
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              "    <tr>\n",
              "      <th>February</th>\n",
              "      <td>13793.306352</td>\n",
              "      <td>13869.217293</td>\n",
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              "    <tr>\n",
              "      <th>March</th>\n",
              "      <td>14790.177346</td>\n",
              "      <td>14833.352724</td>\n",
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              "    <tr>\n",
              "      <th>April</th>\n",
              "      <td>15132.974493</td>\n",
              "      <td>15119.381881</td>\n",
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              "      <td>10708.139980</td>\n",
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              "    <tr>\n",
              "      <th>June</th>\n",
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              "      <td>9294.420703</td>\n",
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              "      <th>July</th>\n",
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              "      <td>9330.128271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>August</th>\n",
              "      <td>11312.971706</td>\n",
              "      <td>11345.157739</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>September</th>\n",
              "      <td>10489.365578</td>\n",
              "      <td>10462.378150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>October</th>\n",
              "      <td>11321.578327</td>\n",
              "      <td>11416.077925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>November</th>\n",
              "      <td>12542.362183</td>\n",
              "      <td>12537.441752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>December</th>\n",
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              "                                                     [key], {});\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "names = cycle(['Stock Open Price','Stock Close Price','Stock High Price','Stock Low Price'])\n",
        "\n",
        "fig = px.line(y_overall, x=y_overall.Date, y=[y_overall['Open'], y_overall['Close'], \n",
        "                                          y_overall['High'], y_overall['Low']],\n",
        "             labels={'Date': 'Date','value':'Stock value'})\n",
        "fig.update_layout(title_text='Stock analysis chart', font_size=15, font_color='black',legend_title_text='Stock Parameters')\n",
        "fig.for_each_trace(lambda t:  t.update(name = next(names)))\n",
        "fig.update_xaxes(showgrid=False)\n",
        "fig.update_yaxes(showgrid=False)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "WutspwAoJ-fE",
        "outputId": "922ec85c-1613-4a36-fa34-d49dde6460cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    },
    {
      "cell_type": "code",
      "source": [
        "#LSTM Model\n",
        "# First Take all the Close Price \n",
        "closedf = maindf[['Date','Close']]\n",
        "print(\"Shape of close dataframe:\", closedf.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hcff_BtUKJNz",
        "outputId": "e5645a93-2b62-416f-d39f-200737ca2a16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of close dataframe: (2812, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.line(closedf, x=closedf.Date, y=closedf.Close,labels={'date':'Date','close':'Close Stock'})\n",
        "fig.update_traces(marker_line_width=2, opacity=0.8, marker_line_color='orange')\n",
        "fig.update_layout(title_text='Whole period of timeframe of Bitcoin close price 2014-2022', plot_bgcolor='white', \n",
        "                  font_size=15, font_color='black')\n",
        "fig.update_xaxes(showgrid=False)\n",
        "fig.update_yaxes(showgrid=False)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "9FIojsIMKQz_",
        "outputId": "f87c7bd1-b033-48cf-f10a-8c869ccd0508"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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period of timeframe of Bitcoin close price 2014-2022\"},\"font\":{\"size\":15,\"color\":\"black\"},\"plot_bgcolor\":\"white\"},                        {\"responsive\": true}                    ).then(function(){\n",
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              "</body>\n",
              "</html>"
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data of just 2 Year\n",
        "\n",
        "closedf = closedf[closedf['Date'] > '2020-05-30']\n",
        "close_stock = closedf.copy()\n",
        "print(\"Total data for prediction: \",closedf.shape[0])\n",
        "closedf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "Ye9oXmH6KTfm",
        "outputId": "0cc253d9-02cc-40af-e106-21a43bc48418"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total data for prediction:  729\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date         Close\n",
              "2083 2020-05-31   9461.058594\n",
              "2084 2020-06-01  10167.268555\n",
              "2085 2020-06-02   9529.803711\n",
              "2086 2020-06-03   9656.717773\n",
              "2087 2020-06-04   9800.636719\n",
              "...         ...           ...\n",
              "2807 2022-05-25  29562.361328\n",
              "2808 2022-05-26  29267.224609\n",
              "2809 2022-05-27  28627.574219\n",
              "2810 2022-05-28  28814.900391\n",
              "2812 2022-05-30  30655.199219\n",
              "\n",
              "[729 rows x 2 columns]"
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Close</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2083</th>\n",
              "      <td>2020-05-31</td>\n",
              "      <td>9461.058594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2084</th>\n",
              "      <td>2020-06-01</td>\n",
              "      <td>10167.268555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2085</th>\n",
              "      <td>2020-06-02</td>\n",
              "      <td>9529.803711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2086</th>\n",
              "      <td>2020-06-03</td>\n",
              "      <td>9656.717773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2087</th>\n",
              "      <td>2020-06-04</td>\n",
              "      <td>9800.636719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2807</th>\n",
              "      <td>2022-05-25</td>\n",
              "      <td>29562.361328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2808</th>\n",
              "      <td>2022-05-26</td>\n",
              "      <td>29267.224609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2809</th>\n",
              "      <td>2022-05-27</td>\n",
              "      <td>28627.574219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2810</th>\n",
              "      <td>2022-05-28</td>\n",
              "      <td>28814.900391</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2812</th>\n",
              "      <td>2022-05-30</td>\n",
              "      <td>30655.199219</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>729 rows × 2 columns</p>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
          "execution_count": 69
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.line(closedf, x=closedf.Date, y=closedf.Close,labels={'date':'Date','close':'Close Stock'})\n",
        "fig.update_traces(marker_line_width=2, opacity=0.8, marker_line_color='orange')\n",
        "fig.update_layout(title_text='Considered period to predict Bitcoin close price', \n",
        "                  plot_bgcolor='white', font_size=15, font_color='black')\n",
        "fig.update_xaxes(showgrid=False)\n",
        "fig.update_yaxes(showgrid=False)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "wZDahiQEKlFK",
        "outputId": "44f0b9c4-5a25-4a3f-cc9a-5d8a2e645821"
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      "execution_count": null,
      "outputs": [
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# deleting date column and normalizing using MinMax Scaler\n",
        "\n",
        "del closedf['Date']\n",
        "scaler=MinMaxScaler(feature_range=(0,1))\n",
        "closedf=scaler.fit_transform(np.array(closedf).reshape(-1,1))\n",
        "print(closedf.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dYw7bOLOKn8t",
        "outputId": "419bde67-3f8b-40a8-86ef-962644013f86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(729, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# we keep the training set as 70% and 30% testing set\n",
        "\n",
        "training_size=int(len(closedf)*0.70)\n",
        "test_size=len(closedf)-training_size\n",
        "train_data,test_data=closedf[0:training_size,:],closedf[training_size:len(closedf),:1]\n",
        "print(\"train_data: \", train_data.shape)\n",
        "print(\"test_data: \", test_data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MCP5tD48KtCg",
        "outputId": "2f0d697c-8779-4bad-d044-00333b5ebe43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train_data:  (510, 1)\n",
            "test_data:  (219, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert an array of values into a dataset matrix\n",
        "\n",
        "def create_dataset(dataset, time_step=1):\n",
        "    dataX, dataY = [], []\n",
        "    for i in range(len(dataset)-time_step-1):\n",
        "        a = dataset[i:(i+time_step), 0]   ###i=0, 0,1,2,3-----99   100 \n",
        "        dataX.append(a)\n",
        "        dataY.append(dataset[i + time_step, 0])\n",
        "    return np.array(dataX), np.array(dataY)"
      ],
      "metadata": {
        "id": "XErNhM8SK0hq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "time_step = 30\n",
        "X_train, y_train = create_dataset(train_data, time_step)\n",
        "X_test, y_test = create_dataset(test_data, time_step)\n",
        "\n",
        "print(\"X_train: \", X_train.shape)\n",
        "print(\"y_train: \", y_train.shape)\n",
        "print(\"X_test: \", X_test.shape)\n",
        "print(\"y_test\", y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LB_7ZKSxK4gn",
        "outputId": "f417a926-abb6-4a3d-bd6d-4828937605dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train:  (479, 30)\n",
            "y_train:  (479,)\n",
            "X_test:  (188, 30)\n",
            "y_test (188,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# reshape input to be [samples, time steps, features] which is required for LSTM\n",
        "X_train =X_train.reshape(X_train.shape[0],X_train.shape[1] , 1)\n",
        "X_test = X_test.reshape(X_test.shape[0],X_test.shape[1] , 1)\n",
        "\n",
        "print(\"X_train: \", X_train.shape)\n",
        "print(\"X_test: \", X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VYIj4tpuK56c",
        "outputId": "732f03ee-fec8-4ecd-faa3-ffb628553839"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train:  (479, 30, 1)\n",
            "X_test:  (188, 30, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Actuall Model Building\n",
        "model=Sequential()\n",
        "\n",
        "model.add(LSTM(10,input_shape=(None,1),activation=\"relu\"))\n",
        "\n",
        "model.add(Dense(1))\n",
        "\n",
        "model.compile(loss=\"mean_squared_error\",optimizer=\"adam\")"
      ],
      "metadata": {
        "id": "iosLuy1AK73e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=200,batch_size=32,verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PyCONeDjLB-l",
        "outputId": "f087431e-5e97-438c-b536-327e843061e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "15/15 [==============================] - 2s 46ms/step - loss: 0.2738 - val_loss: 0.2780\n",
            "Epoch 2/200\n",
            "15/15 [==============================] - 0s 13ms/step - loss: 0.1783 - val_loss: 0.1691\n",
            "Epoch 3/200\n",
            "15/15 [==============================] - 0s 13ms/step - loss: 0.1012 - val_loss: 0.0621\n",
            "Epoch 4/200\n",
            "15/15 [==============================] - 0s 14ms/step - loss: 0.0303 - val_loss: 0.0074\n",
            "Epoch 5/200\n",
            "15/15 [==============================] - 0s 14ms/step - loss: 0.0128 - val_loss: 0.0073\n",
            "Epoch 6/200\n",
            "15/15 [==============================] - 0s 13ms/step - loss: 0.0108 - val_loss: 0.0057\n",
            "Epoch 7/200\n",
            "15/15 [==============================] - 0s 13ms/step - loss: 0.0101 - val_loss: 0.0065\n",
            "Epoch 8/200\n",
            "15/15 [==============================] - 0s 13ms/step - loss: 0.0098 - val_loss: 0.0059\n",
            "Epoch 9/200\n",
            "15/15 [==============================] - 0s 14ms/step - loss: 0.0094 - val_loss: 0.0059\n",
            "Epoch 10/200\n",
            "15/15 [==============================] - 0s 14ms/step - loss: 0.0091 - val_loss: 0.0055\n",
            "Epoch 11/200\n",
            "15/15 [==============================] - 0s 14ms/step - loss: 0.0088 - val_loss: 0.0055\n",
            "Epoch 12/200\n",
            "15/15 [==============================] - 0s 13ms/step - loss: 0.0085 - val_loss: 0.0051\n",
            "Epoch 13/200\n",
            "15/15 [==============================] - 0s 16ms/step - loss: 0.0082 - val_loss: 0.0050\n",
            "Epoch 14/200\n",
            "15/15 [==============================] - 0s 13ms/step - loss: 0.0078 - val_loss: 0.0047\n",
            "Epoch 15/200\n",
            "15/15 [==============================] - 0s 13ms/step - loss: 0.0075 - val_loss: 0.0048\n",
            "Epoch 16/200\n",
            "15/15 [==============================] - 0s 14ms/step - loss: 0.0072 - val_loss: 0.0043\n",
            "Epoch 17/200\n",
            "15/15 [==============================] - 0s 13ms/step - loss: 0.0069 - val_loss: 0.0043\n",
            "Epoch 18/200\n",
            "15/15 [==============================] - 0s 13ms/step - loss: 0.0067 - val_loss: 0.0040\n",
            "Epoch 19/200\n",
            "15/15 [==============================] - 0s 13ms/step - loss: 0.0064 - val_loss: 0.0039\n",
            "Epoch 20/200\n",
            "15/15 [==============================] - 0s 15ms/step - loss: 0.0061 - val_loss: 0.0036\n",
            "Epoch 21/200\n",
            "15/15 [==============================] - 0s 14ms/step - loss: 0.0058 - val_loss: 0.0035\n",
            "Epoch 22/200\n",
            "15/15 [==============================] - 0s 14ms/step - loss: 0.0056 - val_loss: 0.0033\n",
            "Epoch 23/200\n",
            "15/15 [==============================] - 0s 15ms/step - loss: 0.0053 - val_loss: 0.0032\n",
            "Epoch 24/200\n",
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            "Epoch 147/200\n",
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            "Epoch 148/200\n",
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            "Epoch 149/200\n",
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            "Epoch 150/200\n",
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            "Epoch 151/200\n",
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            "Epoch 153/200\n",
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            "Epoch 154/200\n",
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            "Epoch 155/200\n",
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            "Epoch 156/200\n",
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            "Epoch 158/200\n",
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            "Epoch 159/200\n",
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            "Epoch 160/200\n",
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            "Epoch 161/200\n",
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            "Epoch 162/200\n",
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            "Epoch 163/200\n",
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            "Epoch 164/200\n",
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            "Epoch 165/200\n",
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            "Epoch 166/200\n",
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            "Epoch 167/200\n",
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            "Epoch 168/200\n",
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            "Epoch 169/200\n",
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            "Epoch 170/200\n",
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            "Epoch 171/200\n",
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            "Epoch 172/200\n",
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            "Epoch 173/200\n",
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            "Epoch 174/200\n",
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            "Epoch 175/200\n",
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            "Epoch 176/200\n",
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            "Epoch 177/200\n",
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            "Epoch 178/200\n",
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            "Epoch 179/200\n",
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            "Epoch 180/200\n",
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            "Epoch 181/200\n",
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            "Epoch 182/200\n",
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            "Epoch 183/200\n",
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            "Epoch 184/200\n",
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            "Epoch 185/200\n",
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            "Epoch 186/200\n",
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            "Epoch 187/200\n",
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            "Epoch 188/200\n",
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            "Epoch 189/200\n",
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            "Epoch 190/200\n",
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            "Epoch 191/200\n",
            "15/15 [==============================] - 0s 14ms/step - loss: 9.0066e-04 - val_loss: 8.9793e-04\n",
            "Epoch 192/200\n",
            "15/15 [==============================] - 0s 13ms/step - loss: 9.0386e-04 - val_loss: 7.8757e-04\n",
            "Epoch 193/200\n",
            "15/15 [==============================] - 0s 14ms/step - loss: 8.9930e-04 - val_loss: 8.6116e-04\n",
            "Epoch 194/200\n",
            "15/15 [==============================] - 0s 14ms/step - loss: 8.7830e-04 - val_loss: 8.3574e-04\n",
            "Epoch 195/200\n",
            "15/15 [==============================] - 0s 15ms/step - loss: 8.8097e-04 - val_loss: 7.8592e-04\n",
            "Epoch 196/200\n",
            "15/15 [==============================] - 0s 13ms/step - loss: 9.0176e-04 - val_loss: 8.9389e-04\n",
            "Epoch 197/200\n",
            "15/15 [==============================] - 0s 13ms/step - loss: 8.7455e-04 - val_loss: 8.2780e-04\n",
            "Epoch 198/200\n",
            "15/15 [==============================] - 0s 14ms/step - loss: 8.7659e-04 - val_loss: 7.9002e-04\n",
            "Epoch 199/200\n",
            "15/15 [==============================] - 0s 15ms/step - loss: 8.8620e-04 - val_loss: 8.5396e-04\n",
            "Epoch 200/200\n",
            "15/15 [==============================] - 0s 16ms/step - loss: 8.7121e-04 - val_loss: 8.3111e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting Loss vs Validation loss \n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(len(loss))\n",
        "\n",
        "plt.plot(epochs, loss, 'r', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend(loc=0)\n",
        "plt.figure()\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "rjjCxOjSLI3v",
        "outputId": "0fda76d6-baf1-4341-beb9-2a3dac515466"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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f/jLTpk3j5JNP5vTTT2+0rC1btjBr1iweffRRDjroIM455xx+/OMfc9llyYnXhgwZwtKlS7n11lu56aab+PnPf97i31fqw+yWZw99wADYuTM5DICZlZX8YZf84Zb77ruP8ePHM27cOJYvX95oeKSpJ598klNPPZW+ffsyYMAApk2b1nDfSy+9xNFHH82YMWO4++67Wb58eav1rFy5kpEjR3LQQQcBcO6557Jo0aKG+2fMmAHAEUcc0XBAr5Y89dRTnH322UDzh9m95ZZb+PDDD6murmbChAncfvvtzJ49mxdffJH+uQMP7oby7aFD0kvv27e0tZiVqVIdPXf69OlcfvnlLF26lE2bNnHEEUfwxhtvcNNNN7F48WL22msvZs2axZYtWzq0/FmzZvHAAw9w+OGHc8cdd/DEE0/sVr25Q/DuzuF3u+owu+XZQ/chdM3KVk1NDcceeyznnXdeQ+98/fr19OvXj4EDB/Luu+/y8MMPt7qMY445hgceeIDNmzezYcMGHnzwwYb7NmzYwH777cf27dsbDnkL0L9/fzY0kxkHH3wwq1evZtWqVQDcddddfOYzn+nQ31bqw+yWdw/dO0bNytLMmTM59dRTG4ZeDj/8cMaNG8chhxzC8OHDmTx5cquPHz9+PF/4whc4/PDD2XvvvRsdAvf6669n0qRJDB06lEmTJjWE+Jlnnsn555/PLbfc0rAzFKBPnz7cfvvtnHHGGdTX1zNhwgQuuuiiDv1dpT7MbvkdPhfgscfguOPgiSegg++kZpXIh88tL9k/fC54yMXMrBkOdDOzjCjPQM+dhs5j6GbtVqphVmufjjxP5Rno7qGbdUifPn1Yu3atQ72biwjWrl1Lnz592vW48vyWS79+ybUD3axdhg0bRl1dHWvWrCl1KdaGPn36MGzYsHY9pjwDvUcPqKnxkItZO/Xs2ZORI0eWugzrJOU55AKw117QzGE0zcwqVUGBLukESSslrZJ0VTP3z5K0RtKy9PLF4pfaxKBB8P77nb4aM7Ny0eaQi6QqYA7wN0AdsFjS/IhoeuSceyPikk6osXkOdDOzRgrpoU8EVkXE6xGxDZgHTO/csgrgQDcza6SQQN8feCvvdl06r6nTJL0g6X5Jw4tSXWsc6GZmjRRrp+iDwIiIOAz4D+DO5hpJukDSEklLdvtrU7lA9/dpzcyAwgL9bSC/xz0sndcgItZGxNb05s+BI5pbUETMjYjaiKgdOnRoR+rdZdAg2LYNinCWDzOzLCgk0BcDoySNlNQLOBOYn99A0n55N6cBrxSvxBYMGpRce9jFzAwo4FsuEVEv6RJgIVAF/CIilku6DlgSEfOBSyVNA+qB94FZnVhzIj/Qh3f+kL2ZWXdX0C9FI2IBsKDJvGvypr8JfLO4pbUhF+hr13bpas3Muqvy/aWoh1zMzBop30AfPDi5dqCbmQHlHOjuoZuZNVK+gb7HHtCnjwPdzCxVvoEO/rWomVkeB7qZWUY40M3MMsKBbmaWEQ50M7OMcKCbmWVE+Qf65s3JxcyswpV3oO+1V3L9wQelrcPMrBso70CvqUmuN24sbR1mZt1ANgL9L38pbR1mZt1AeQd6v37JtXvoZmZlHujuoZuZNSjvQHcP3cysQXkHunvoZmYNyjvQ3UM3M2tQ3oHuHrqZWYPyDvQ99kiu3UM3MyvzQO/RIxl2cQ/dzKzMAx2SQHcP3cwsA4FeU+MeupkZBQa6pBMkrZS0StJVrbQ7TVJIqi1eiW1wD93MDCgg0CVVAXOAqcBoYKak0c206w98BXi22EW2yj10MzOgsB76RGBVRLweEduAecD0ZtpdD/wA2FLE+trmHrqZGVBYoO8PvJV3uy6d10DSeGB4RDzU2oIkXSBpiaQla9asaXexzXIP3cwMKMJOUUk9gB8BX22rbUTMjYjaiKgdOnTo7q464R66mRlQWKC/DQzPuz0snZfTH/gk8ISk1cCRwPwu2zHqHrqZGVBYoC8GRkkaKakXcCYwP3dnRKyLiCERMSIiRgDPANMiYkmnVNyUe+hmZkABgR4R9cAlwELgFeC+iFgu6TpJ0zq7wDbV1MCmTbBzZ6krMTMrqepCGkXEAmBBk3nXtNB2yu6X1Q65Iy5u3rxr2sysAmXjl6LgYRczq3jlH+i5Xrl3jJpZhSv/QHcP3cwMyEKgu4duZgZkIdDdQzczA7IQ6O6hm5kBWQh099DNzIAsBLp76GZmQJYC3T10M6tw2Ql099DNrMKVf6BXV0Pv3u6hm1nFK/9ABx9C18yMrAS6D6FrZpahQN+0qdRVmJmVVDYCvW9fB7qZVTwHuplZRjjQzcwywoFuZpYRDnQzs4zIRqD7Wy5mZhkJdPfQzcwc6GZmWZGdQK+vh23bSl2JmVnJZCfQwb10M6toBQW6pBMkrZS0StJVzdx/kaQXJS2T9JSk0cUvtRUOdDOztgNdUhUwB5gKjAZmNhPY90TEmIgYC/wQ+FHRK22NA93MrKAe+kRgVUS8HhHbgHnA9PwGEbE+72Y/IIpXYgEc6GZmVBfQZn/grbzbdcCkpo0kfQm4AugFfLa5BUm6ALgA4IADDmhvrS1zoJuZFW+naETMiYi/Ar4BfLuFNnMjojYiaocOHVqsVTvQzcwoLNDfBobn3R6WzmvJPOCU3Smq3XLnFXWgm1kFKyTQFwOjJI2U1As4E5if30DSqLybJwGvFq/EAriHbmbW9hh6RNRLugRYCFQBv4iI5ZKuA5ZExHzgEknHA9uBD4BzO7Poj3Cgm5kVtFOUiFgALGgy75q86a8Uua72yQW6TxRtZhXMvxQ1M8uIbAT6Hnsk1w50M6tg2Qj0qiro3duBbmYVLRuBDj6ErplVPAe6mVlGONDNzDIiO4Hu84qaWYXLTqC7h25mFS5bge4fFplZBctWoLuHbmYVzIFuZpYRDnQzs4xwoJuZZYQD3cwsI7IV6Fu3wo4dpa7EzKwkshXoAJs3l7YOM7MSyU6g+7yiZlbhshPoPsmFmVW47AW6fy1qZhUqe4HuHrqZVSgHuplZRjjQzcwywoFuZpYRDnQzs4woKNAlnSBppaRVkq5q5v4rJL0s6QVJj0r6ePFLbYMD3cwqXJuBLqkKmANMBUYDMyWNbtLseaA2Ig4D7gd+WOxC2+RAN7MKV0gPfSKwKiJej4htwDxgen6DiHg8InJJ+gwwrLhlFsC/FDWzCldIoO8PvJV3uy6d15K/Bx5u7g5JF0haImnJmjVrCq+yED17QnW1A93MKlZRd4pK+u9ALXBjc/dHxNyIqI2I2qFDhxZz1QmfV9TMKlh1AW3eBobn3R6WzmtE0vHA1cBnImJrccprJx8T3cwqWCE99MXAKEkjJfUCzgTm5zeQNA74KTAtIv5c/DIL5EA3swrWZqBHRD1wCbAQeAW4LyKWS7pO0rS02Y1ADfCvkpZJmt/C4jqXA93MKlghQy5ExAJgQZN51+RNH1/kujrGgW5mFSw7vxQFB7qZVTQHuplZRjjQzcwyIluB3q+fA93MKla2At09dDOrYNkLdP9S1MwqVPYCfdMmiCh1JWZmXS57gQ6wZUtp6zAzK4FsBrrH0c2sAjnQzcwywoFuZpYRDnQzs4xwoJuZZUS2At3nFTWzCpatQHcP3cwqWDYD3b8WNbMKlM1Adw/dzCqQA93MLCMc6GZmGZGtQO/TJ7l2oJtZBcpWoEs+JrqZVaxsBTo40M2sYjnQzcwyoqBAl3SCpJWSVkm6qpn7j5G0VFK9pNOLX2Y7+LyiZlah2gx0SVXAHGAqMBqYKWl0k2b/D5gF3FPsAtutXz/YsKHUVZiZdbnqAtpMBFZFxOsAkuYB04GXcw0iYnV6385OqLF9BgxwoJtZRSpkyGV/4K2823XpvO5pwABYv77UVZiZdbku3Skq6QJJSyQtWbNmTeesxIFuZhWqkEB/Gxied3tYOq/dImJuRNRGRO3QoUM7soi2DRwI69Z1zrLNzLqxQgJ9MTBK0khJvYAzgfmdW9ZuyPXQI0pdiZlZl2oz0COiHrgEWAi8AtwXEcslXSdpGoCkCZLqgDOAn0pa3plFt2rAANi5019dNLOKU8i3XIiIBcCCJvOuyZteTDIUU3oDBiTX69fvOoORmVkFyN4vRfMD3cysgjjQzcwywoFuZpYR2Qv0gQOTawe6mVWY7AV6rofu76KbWYXJbqC7h25mFSZ7gd6/f3LtQDezCpO9QO/VKzm3qAPdzCpM9gIdfIAuM6tI2Qz0gQMd6GZWcbIZ6O6hm1kFcqCbmWWEA93MLCOyG+j+YZGZVZjsBrp76GZWYbId6D5rkZlVkGwG+sCBUF8PW7aUuhIzsy6TzUD38VzMrAI50M3MMiLbgb5mTWnrMDPrQtkM9IkToXdvuOuuUldiZtZlshno++wDZ58Nd9zhXrqZVYxsBjrAFVck33K5/nrYsaPU1ZiZdbrqUhfQaT7xiaSX/k//BAsXwtFHw777Qs+euy69ejW+nT+vV6/G083Na3p/z57QI7vvkWbWvRUU6JJOAP4XUAX8PCL+scn9vYFfAkcAa4EvRMTq4pbaAXfeCaecArfcAg8+CO+9Bzt3du46e/eGPfZILn377prOv92/f7LjdsCA5Dvz/fsn9/XtC/367ZoeMABGjkyWaWbWhjYDXVIVMAf4G6AOWCxpfkS8nNfs74EPIuKvJZ0J/AD4QmcU3C4SzJiRXHJ27IDt21u+bNvW8nVL07nrrVuTYZ7Nm5PLpk27pjdvhvffT+Zt2JB8pXLdurbfYKqq4MAD4ZBDknAfMiQJ+N69k08G+dNVVVBdnVxXVTX+tCA1vu7RY9dje/dO5udfevT46Lyml9zydqdNc7Xlrot5X0fam5WZQnroE4FVEfE6gKR5wHQgP9CnA7PT6fuBf5akiG742/tc2PXpU+pKkkMTbNqUhHvuDSD/8v77sHIlrFiRXB5/HDZuLHXVlaer32h27kw6CfX1ybzq6l1v1Lk32vw33M7Q3Bt7S2/y+fL/5ds73ZydO5NO2I4du6ZzteS2R266uW3RdF5bt9vzmOY6LbnpnTuT5y932bEj+XReXZ38r3//+8mQcJEVEuj7A2/l3a4DJrXUJiLqJa0DBgPv5TeSdAFwAcABBxzQwZIzREqGWPr1K6x9RPLC2Lo1ueQ+FeSmcy/83Aso98/S9BqS+7dtSz5RbN2a3Jd/2bnzo/PyL7nlFaNNSzW2Vn977iu3ZeU+aVWn/565T4+55yT/ujO09lpo+rrItW/uDaoj001Juzph+Z86cwGfH/jN/R3tud2exzT3Gs+/X0r2qeWexx49kv+17duTYB8+vOW/eTd06U7RiJgLzAWora3tfr337i6/t1bom4CZVYxCvpLxNpD/djIsnddsG0nVwECSnaNmZtZFCgn0xcAoSSMl9QLOBOY3aTMfODedPh14rFuOn5uZZVibQy7pmPglwEKSry3+IiKWS7oOWBIR84HbgLskrQLeJwl9MzPrQgWNoUfEAmBBk3nX5E1vAc4obmlmZtYe/lmjmVlGONDNzDLCgW5mlhEOdDOzjFCpvl0oaQ3wZgcfPoQmv0LtRrprba6rfVxX+3XX2rJW18cjYmhzd5Qs0HeHpCURUVvqOprTXWtzXe3jutqvu9ZWSXV5yMXMLCMc6GZmGVGugT631AW0orvW5rrax3W1X3etrWLqKssxdDMz+6hy7aGbmVkTDnQzs4wou0CXdIKklZJWSbqqhHUMl/S4pJclLZf0lXT+bElvS1qWXk4sQW2rJb2Yrn9JOm+QpP+Q9Gp6vVcX13Rw3jZZJmm9pMtKtb0k/ULSnyW9lDev2W2kxC3pa+4FSeO7uK4bJa1I1/1rSXum80dI2py37X7SxXW1+NxJ+ma6vVZK+lxn1dVKbffm1bVa0rJ0fpdss1byoXNfYxFRNheSw/e+BhwI9AL+Cxhdolr2A8an0/2BPwCjSc6t+rUSb6fVwJAm834IXJVOXwX8oMTP45+Aj5dqewHHAOOBl9raRsCJwMOAgCOBZ7u4rv8GVKfTP8ira0R+uxJsr2afu/T/4L+A3sDI9H+2qitra3L//wSu6cpt1ko+dOprrNx66A0nrI6IbUDuhNVdLiLeiYil6fQG4BWSc6t2V9OBO9PpO58xB6sAAAL2SURBVIFTSljLccBrEdHRXwrvtohYRHLs/nwtbaPpwC8j8Qywp6T9uqquiPhNRNSnN58hOWtYl2phe7VkOjAvIrZGxBvAKpL/3S6vTZKAzwP/0lnrb6GmlvKhU19j5RbozZ2wuuQhKmkEMA54Np11Sfqx6RddPbSRCuA3kp5TcmJugH0i4p10+k/APiWoK+dMGv+DlXp75bS0jbrT6+48kp5czkhJz0v6raSjS1BPc89dd9peRwPvRsSrefO6dJs1yYdOfY2VW6B3O5JqgF8Bl0XEeuDHwF8BY4F3SD7udbVPR8R4YCrwJUnH5N8ZyWe8knxfVclpDKcB/5rO6g7b6yNKuY1aIulqoB64O531DnBARIwDrgDukTSgC0vqls9dEzNp3Hno0m3WTD406IzXWLkFeiEnrO4yknqSPFl3R8S/AUTEuxGxIyJ2Aj+jEz9qtiQi3k6v/wz8Oq3h3dxHuPT6z11dV2oqsDQi3k1rLPn2ytPSNir5607SLOBk4Kw0CEiHNNam08+RjFUf1FU1tfLclXx7QcMJ62cA9+bmdeU2ay4f6OTXWLkFeiEnrO4S6djcbcArEfGjvPn5416nAi81fWwn19VPUv/cNMkOtZdofCLvc4H/05V15WnUYyr19mqipW00Hzgn/SbCkcC6vI/NnU7SCcCVwLSI2JQ3f6ikqnT6QGAU8HoX1tXSczcfOFNSb0kj07p+31V15TkeWBERdbkZXbXNWsoHOvs11tl7e4t9Idkb/AeSd9arS1jHp0k+Lr0ALEsvJwJ3AS+m8+cD+3VxXQeSfMPgv4DluW0EDAYeBV4FHgEGlWCb9QPWAgPz5pVke5G8qbwDbCcZr/z7lrYRyTcP5qSvuReB2i6uaxXJ+GrudfaTtO1p6XO8DFgK/G0X19XicwdcnW6vlcDUrn4u0/l3ABc1adsl26yVfOjU15h/+m9mlhHlNuRiZmYtcKCbmWWEA93MLCMc6GZmGeFANzPLCAe6mVlGONDNzDLi/wNeHQKpYPrf6QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The prediction and check performance metrics\n",
        "train_predict=model.predict(X_train)\n",
        "test_predict=model.predict(X_test)\n",
        "train_predict.shape, test_predict.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "llUxDXIqLRB4",
        "outputId": "bbfd76cc-670c-4a9c-da6d-3a30d81c9609"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((479, 1), (188, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Evaluation\n",
        "# Transform back to original form\n",
        "\n",
        "train_predict = scaler.inverse_transform(train_predict)\n",
        "test_predict = scaler.inverse_transform(test_predict)\n",
        "original_ytrain = scaler.inverse_transform(y_train.reshape(-1,1)) \n",
        "original_ytest = scaler.inverse_transform(y_test.reshape(-1,1)) "
      ],
      "metadata": {
        "id": "2Bv3ba-lLVQL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation metrices RMSE and MAE\n",
        "print(\"Train data Root-mean-square deviation: \", math.sqrt(mean_squared_error(original_ytrain,train_predict)))\n",
        "print(\"Train data Mean squared error: \", mean_squared_error(original_ytrain,train_predict))\n",
        "print(\"Train data measure of errors: \", mean_absolute_error(original_ytrain,train_predict))\n",
        "print(\"-------------------------------------------------------------------------------------\")\n",
        "print(\"Test data Root-mean-square deviation: \", math.sqrt(mean_squared_error(original_ytest,test_predict)))\n",
        "print(\"Test data Mean squared error: \", mean_squared_error(original_ytest,test_predict))\n",
        "print(\"Test data measure of errors: \", mean_absolute_error(original_ytest,test_predict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aJWhjPcOLeqj",
        "outputId": "4a4b9f0f-5d19-447c-d9c8-222c766d11cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train data Root-mean-square deviation:  1720.1624441173572\n",
            "Train data Mean squared error:  2958958.8341518\n",
            "Train data measure of errors:  1184.5165628397704\n",
            "-------------------------------------------------------------------------------------\n",
            "Test data Root-mean-square deviation:  1687.1153282791906\n",
            "Test data Mean squared error:  2846358.130914601\n",
            "Test data measure of errors:  1282.4875955405587\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Variance Regression Score\n",
        "\n",
        "print(\"Train data explained variance regression score:\", \n",
        "      explained_variance_score(original_ytrain, train_predict))\n",
        "print(\"Test data explained variance regression score:\", \n",
        "      explained_variance_score(original_ytest, test_predict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9HAduvydL7Q9",
        "outputId": "75c65452-6428-4454-dfdc-227b28571279"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train data explained variance regression score: 0.9901621797882803\n",
            "Test data explained variance regression score: 0.9397413351879735\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# R square score for regression\n",
        "print(\"Train data R2 score:\", r2_score(original_ytrain, train_predict))\n",
        "print(\"Test data R2 score:\", r2_score(original_ytest, test_predict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EMZaq_8LL_nY",
        "outputId": "ef79b8fa-7b06-433e-fa21-601615abfa74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train data R2 score: 0.9901618162865842\n",
            "Test data R2 score: 0.9338064854630307\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Regression Loss Mean Gamma deviance regression loss (MGD) and Mean Poisson deviance regression loss (MPD)\n",
        "print(\"Train data Meibomian gland dysfunction: \", mean_gamma_deviance(original_ytrain, train_predict))\n",
        "print(\"Test data Meibomian gland dysfunction: \", mean_gamma_deviance(original_ytest, test_predict))\n",
        "print(\"----------------------------------------------------------------------\")\n",
        "print(\"Train data Mean Poisson deviance: \", mean_poisson_deviance(original_ytrain, train_predict))\n",
        "print(\"Test data Mean Poisson deviance: \", mean_poisson_deviance(original_ytest, test_predict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Tr72ZPYMJdX",
        "outputId": "92b0b883-861f-4ca3-fe7e-dd545513cbb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train data Meibomian gland dysfunction:  0.00203966043995268\n",
            "Test data Meibomian gland dysfunction:  0.0016789408312622989\n",
            "----------------------------------------------------------------------\n",
            "Train data Mean Poisson deviance:  71.51539818955078\n",
            "Test data Mean Poisson deviance:  68.33551039071716\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Comparision of original stock close price and predicted close price\n",
        "\n",
        "# shift train predictions for plotting\n",
        "\n",
        "look_back=time_step\n",
        "trainPredictPlot = np.empty_like(closedf)\n",
        "trainPredictPlot[:, :] = np.nan\n",
        "trainPredictPlot[look_back:len(train_predict)+look_back, :] = train_predict\n",
        "print(\"Train predicted data: \", trainPredictPlot.shape)\n",
        "\n",
        "# shift test predictions for plotting\n",
        "testPredictPlot = np.empty_like(closedf)\n",
        "testPredictPlot[:, :] = np.nan\n",
        "testPredictPlot[len(train_predict)+(look_back*2)+1:len(closedf)-1, :] = test_predict\n",
        "print(\"Test predicted data: \", testPredictPlot.shape)\n",
        "\n",
        "names = cycle(['Original close price','Train predicted close price','Test predicted close price'])\n",
        "\n",
        "\n",
        "plotdf = pd.DataFrame({'date': close_stock['Date'],\n",
        "                       'original_close': close_stock['Close'],\n",
        "                      'train_predicted_close': trainPredictPlot.reshape(1,-1)[0].tolist(),\n",
        "                      'test_predicted_close': testPredictPlot.reshape(1,-1)[0].tolist()})\n",
        "\n",
        "fig = px.line(plotdf,x=plotdf['date'], y=[plotdf['original_close'],plotdf['train_predicted_close'],\n",
        "                                          plotdf['test_predicted_close']],\n",
        "              labels={'value':'Stock price','date': 'Date'})\n",
        "fig.update_layout(title_text='Comparision between original close price vs predicted close price',\n",
        "                  plot_bgcolor='white', font_size=15, font_color='black', legend_title_text='Close Price')\n",
        "fig.for_each_trace(lambda t:  t.update(name = next(names)))\n",
        "\n",
        "fig.update_xaxes(showgrid=False)\n",
        "fig.update_yaxes(showgrid=False)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "rNkI3zD6Mijo",
        "outputId": "f41e0c41-ccfc-4e84-85cf-3b8d92726a30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train predicted data:  (729, 1)\n",
            "Test predicted data:  (729, 1)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
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              "<body>\n",
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Predicting next 15 days\n",
        "\n",
        "x_input=test_data[len(test_data)-time_step:].reshape(1,-1)\n",
        "temp_input=list(x_input)\n",
        "temp_input=temp_input[0].tolist()\n",
        "\n",
        "from numpy import array\n",
        "\n",
        "lst_output=[]\n",
        "n_steps=time_step\n",
        "i=0\n",
        "pred_days = 15\n",
        "while(i<pred_days):\n",
        "    \n",
        "    if(len(temp_input)>time_step):\n",
        "        \n",
        "        x_input=np.array(temp_input[1:])\n",
        "        #print(\"{} day input {}\".format(i,x_input))\n",
        "        x_input = x_input.reshape(1,-1)\n",
        "        x_input = x_input.reshape((1, n_steps, 1))\n",
        "        \n",
        "        yhat = model.predict(x_input, verbose=0)\n",
        "        #print(\"{} day output {}\".format(i,yhat))\n",
        "        temp_input.extend(yhat[0].tolist())\n",
        "        temp_input=temp_input[1:]\n",
        "        #print(temp_input)\n",
        "       \n",
        "        lst_output.extend(yhat.tolist())\n",
        "        i=i+1\n",
        "        \n",
        "    else:\n",
        "        \n",
        "        x_input = x_input.reshape((1, n_steps,1))\n",
        "        yhat = model.predict(x_input, verbose=0)\n",
        "        temp_input.extend(yhat[0].tolist())\n",
        "        \n",
        "        lst_output.extend(yhat.tolist())\n",
        "        i=i+1\n",
        "               \n",
        "print(\"Output of predicted next days: \", len(lst_output))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3_-0V-szM2SM",
        "outputId": "c1457c97-fff7-4c7e-82e1-b945c9368454"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Output of predicted next days:  15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#last 30 days of dataset and next predicted 15 days\n",
        "\n",
        "last_days=np.arange(1,time_step+1)\n",
        "day_pred=np.arange(time_step+1,time_step+pred_days+1)\n",
        "print(last_days)\n",
        "print(day_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QlcSq8fXNA1J",
        "outputId": "8e6a9236-0246-455c-f542-0ab5068118eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n",
            " 25 26 27 28 29 30]\n",
            "[31 32 33 34 35 36 37 38 39 40 41 42 43 44 45]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp_mat = np.empty((len(last_days)+pred_days+1,1))\n",
        "temp_mat[:] = np.nan\n",
        "temp_mat = temp_mat.reshape(1,-1).tolist()[0]\n",
        "\n",
        "last_original_days_value = temp_mat\n",
        "next_predicted_days_value = temp_mat\n",
        "\n",
        "last_original_days_value[0:time_step+1] = scaler.inverse_transform(closedf[len(closedf)-time_step:]).reshape(1,-1).tolist()[0]\n",
        "next_predicted_days_value[time_step+1:] = scaler.inverse_transform(np.array(lst_output).reshape(-1,1)).reshape(1,-1).tolist()[0]\n",
        "\n",
        "new_pred_plot = pd.DataFrame({\n",
        "    'last_original_days_value':last_original_days_value,\n",
        "    'next_predicted_days_value':next_predicted_days_value\n",
        "})\n",
        "\n",
        "names = cycle(['Last 30 days close price','Predicted next 15 days close price'])\n",
        "\n",
        "fig = px.line(new_pred_plot,x=new_pred_plot.index, y=[new_pred_plot['last_original_days_value'],\n",
        "                                                      new_pred_plot['next_predicted_days_value']],\n",
        "              labels={'value': 'Stock price','index': 'Timestamp'})\n",
        "fig.update_layout(title_text='Compare last 30 days vs next 15 days',\n",
        "                  plot_bgcolor='white', font_size=15, font_color='black',legend_title_text='Close Price')\n",
        "\n",
        "fig.for_each_trace(lambda t:  t.update(name = next(names)))\n",
        "fig.update_xaxes(showgrid=False)\n",
        "fig.update_yaxes(showgrid=False)\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "85-kGCQ4NGpR",
        "outputId": "0ceba03e-923b-4e02-c363-842e31559d53"
      },
      "execution_count": null,
      "outputs": [
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    {
      "cell_type": "code",
      "source": [
        "# Plotting entire Closing Stock Price with next 30 days period of prediction\n",
        "\n",
        "lstmdf=closedf.tolist()\n",
        "lstmdf.extend((np.array(lst_output).reshape(-1,1)).tolist())\n",
        "lstmdf=scaler.inverse_transform(lstmdf).reshape(1,-1).tolist()[0]\n",
        "\n",
        "names = cycle(['Close price'])\n",
        "\n",
        "fig = px.line(lstmdf,labels={'value': 'Stock price','index': 'Timestamp'})\n",
        "fig.update_layout(title_text='Plotting whole closing stock price with prediction',\n",
        "                  plot_bgcolor='white', font_size=15, font_color='black',legend_title_text='Stock')\n",
        "\n",
        "fig.for_each_trace(lambda t:  t.update(name = next(names)))\n",
        "\n",
        "fig.update_xaxes(showgrid=False)\n",
        "fig.update_yaxes(showgrid=False)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
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        "id": "4ER5ylOmNRUW",
        "outputId": "04374329-a708-400a-f65a-865179ece7b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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